High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

APDe-MVS		99.02 1	98.84 1	99.55 1	99.57 23	98.96 2	99.39 5	98.93 36	97.38 17	99.41 3	99.54 1	96.66 5	99.84 42	98.86 2	99.85 2	99.87 1

CHOSEN 1792x2688		97.12 87	96.80 82	98.08 101	99.30 52	94.56 199	98.05 200	99.71 1	93.57 169	97.09 101	98.91 76	88.17 180	99.89 27	96.87 77	99.56 61	99.81 2

EI-MVSNet-Vis-set		98.47 28	98.39 15	98.69 62	99.46 32	96.49 96	98.30 172	98.69 93	97.21 28	98.84 27	99.36 16	95.41 39	99.78 74	98.62 6	99.65 43	99.80 3

ACMMP_Plus		98.61 13	98.30 25	99.55 1	99.62 21	98.95 3	98.82 82	98.81 60	95.80 72	99.16 12	99.47 4	95.37 40	99.92 13	97.89 32	99.75 29	99.79 4

Regformer-4		98.64 10	98.53 7	98.99 47	99.43 35	97.37 63	98.40 159	98.79 68	97.46 12	99.09 13	99.31 21	95.86 31	99.80 57	98.64 4	99.76 23	99.79 4

HPM-MVS		98.36 34	98.10 36	99.13 38	99.74 7	97.82 49	99.53 1	98.80 67	94.63 125	98.61 40	98.97 65	95.13 49	99.77 79	97.65 44	99.83 7	99.79 4

region2R		98.61 13	98.38 16	99.29 18	99.74 7	98.16 34	99.23 21	98.93 36	96.15 60	98.94 21	99.17 39	95.91 28	99.94 3	97.55 50	99.79 10	99.78 7

Regformer-3		98.59 16	98.50 11	98.86 57	99.43 35	97.05 74	98.40 159	98.68 96	97.43 13	99.06 14	99.31 21	95.80 32	99.77 79	98.62 6	99.76 23	99.78 7

XVS		98.70 5	98.49 12	99.34 13	99.70 15	98.35 22	99.29 14	98.88 47	97.40 14	98.46 45	99.20 35	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

X-MVStestdata		94.06 231	92.30 249	99.34 13	99.70 15	98.35 22	99.29 14	98.88 47	97.40 14	98.46 45	43.50 338	95.90 29	99.89 27	97.85 34	99.74 32	99.78 7

ACMMPR		98.59 16	98.36 18	99.29 18	99.74 7	98.15 35	99.23 21	98.95 33	96.10 65	98.93 25	99.19 38	95.70 33	99.94 3	97.62 45	99.79 10	99.78 7

PGM-MVS		98.49 27	98.23 32	99.27 23	99.72 11	98.08 38	98.99 55	99.49 5	95.43 86	99.03 15	99.32 20	95.56 35	99.94 3	96.80 79	99.77 17	99.78 7

SteuartSystems-ACMMP		98.90 2	98.75 2	99.36 12	99.22 71	98.43 16	99.10 44	98.87 49	97.38 17	99.35 5	99.40 6	97.78 1	99.87 35	97.77 39	99.85 2	99.78 7
Skip Steuart: Steuart Systems R&D Blog.
MPTG		98.55 22	98.25 29	99.46 6	99.76 1	98.64 8	98.55 141	98.74 78	97.27 25	98.02 64	99.39 7	94.81 54	99.96 1	97.91 29	99.79 10	99.77 14

MTAPA		98.58 18	98.29 26	99.46 6	99.76 1	98.64 8	98.90 65	98.74 78	97.27 25	98.02 64	99.39 7	94.81 54	99.96 1	97.91 29	99.79 10	99.77 14

mPP-MVS		98.51 26	98.26 28	99.25 24	99.75 3	98.04 39	99.28 16	98.81 60	96.24 58	98.35 52	99.23 29	95.46 38	99.94 3	97.42 55	99.81 8	99.77 14

HPM-MVS_fast		98.38 32	98.13 35	99.12 40	99.75 3	97.86 46	99.44 4	98.82 57	94.46 132	98.94 21	99.20 35	95.16 48	99.74 85	97.58 47	99.85 2	99.77 14

CP-MVS		98.57 20	98.36 18	99.19 28	99.66 19	97.86 46	99.34 11	98.87 49	95.96 68	98.60 41	99.13 44	96.05 22	99.94 3	97.77 39	99.86 1	99.77 14

HyFIR lowres test		96.90 95	96.49 98	98.14 95	99.33 42	95.56 136	97.38 254	99.65 2	92.34 215	97.61 90	98.20 139	89.29 138	99.10 155	96.97 65	97.60 144	99.77 14

HFP-MVS		98.63 12	98.40 14	99.32 16	99.72 11	98.29 25	99.23 21	98.96 31	96.10 65	98.94 21	99.17 39	96.06 20	99.92 13	97.62 45	99.78 14	99.75 20

#test#		98.54 24	98.27 27	99.32 16	99.72 11	98.29 25	98.98 58	98.96 31	95.65 78	98.94 21	99.17 39	96.06 20	99.92 13	97.21 60	99.78 14	99.75 20

Regformer-1		98.66 8	98.51 10	99.12 40	99.35 37	97.81 50	98.37 161	98.76 74	97.49 10	99.20 10	99.21 32	96.08 19	99.79 69	98.42 16	99.73 34	99.75 20

Regformer-2		98.69 7	98.52 8	99.19 28	99.35 37	98.01 41	98.37 161	98.81 60	97.48 11	99.21 9	99.21 32	96.13 16	99.80 57	98.40 18	99.73 34	99.75 20

CPTT-MVS		97.72 56	97.32 63	98.92 53	99.64 20	97.10 73	99.12 42	98.81 60	92.34 215	98.09 58	99.08 54	93.01 80	99.92 13	96.06 99	99.77 17	99.75 20

MCST-MVS		98.65 9	98.37 17	99.48 5	99.60 22	98.87 5	98.41 158	98.68 96	97.04 38	98.52 44	98.80 84	96.78 4	99.83 43	97.93 28	99.61 48	99.74 25

APD-MVS		98.35 35	98.00 40	99.42 9	99.51 26	98.72 7	98.80 91	98.82 57	94.52 128	99.23 8	99.25 28	95.54 37	99.80 57	96.52 89	99.77 17	99.74 25
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		98.78 3	98.62 4	99.24 25	99.69 17	98.28 27	99.14 37	98.66 106	96.84 43	99.56 2	99.31 21	96.34 10	99.70 91	98.32 20	99.73 34	99.73 27

EI-MVSNet-UG-set		98.41 30	98.34 21	98.61 67	99.45 33	96.32 103	98.28 174	98.68 96	97.17 31	98.74 34	99.37 12	95.25 45	99.79 69	98.57 8	99.54 64	99.73 27

MP-MVS		98.33 38	98.01 39	99.28 20	99.75 3	98.18 33	99.22 27	98.79 68	96.13 62	97.92 73	99.23 29	94.54 59	99.94 3	96.74 81	99.78 14	99.73 27

APD-MVS_3200maxsize		98.53 25	98.33 24	99.15 37	99.50 27	97.92 45	99.15 36	98.81 60	96.24 58	99.20 10	99.37 12	95.30 43	99.80 57	97.73 41	99.67 39	99.72 30

DeepPCF-MVS	96.37 2	97.93 48	98.48 13	96.30 219	99.00 86	89.54 280	97.43 251	98.87 49	98.16 2	99.26 6	99.38 11	96.12 17	99.64 100	98.30 21	99.77 17	99.72 30

NCCC		98.61 13	98.35 20	99.38 10	99.28 60	98.61 10	98.45 153	98.76 74	97.82 3	98.45 48	98.93 73	96.65 6	99.83 43	97.38 57	99.41 76	99.71 32

3Dnovator+	94.38 6	97.43 72	96.78 85	99.38 10	97.83 158	98.52 11	99.37 7	98.71 90	97.09 37	92.99 237	99.13 44	89.36 136	99.89 27	96.97 65	99.57 55	99.71 32

ACMMP		98.23 41	97.95 41	99.09 42	99.74 7	97.62 55	99.03 52	99.41 6	95.98 67	97.60 91	99.36 16	94.45 64	99.93 9	97.14 61	98.85 97	99.70 34
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
HSP-MVS		98.70 5	98.52 8	99.24 25	99.75 3	98.23 28	99.26 17	98.58 119	97.52 7	99.41 3	98.78 85	96.00 23	99.79 69	97.79 38	99.59 52	99.69 35

test9_res														96.39 94	99.57 55	99.69 35

abl_6		98.30 40	98.03 38	99.13 38	99.56 24	97.76 51	99.13 40	98.82 57	96.14 61	99.26 6	99.37 12	93.33 76	99.93 9	96.96 67	99.67 39	99.69 35

CNVR-MVS		98.78 3	98.56 6	99.45 8	99.32 45	98.87 5	98.47 152	98.81 60	97.72 4	98.76 33	99.16 42	97.05 2	99.78 74	98.06 25	99.66 42	99.69 35

MVS_111021_HR		98.47 28	98.34 21	98.88 56	99.22 71	97.32 64	97.91 215	99.58 3	97.20 29	98.33 53	99.00 63	95.99 24	99.64 100	98.05 26	99.76 23	99.69 35

DeepC-MVS_fast	96.70 1	98.55 22	98.34 21	99.18 32	99.25 64	98.04 39	98.50 149	98.78 70	97.72 4	98.92 26	99.28 25	95.27 44	99.82 48	97.55 50	99.77 17	99.69 35
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
train_agg		97.97 44	97.52 54	99.33 15	99.31 47	98.50 12	97.92 212	98.73 83	92.98 188	97.74 81	98.68 94	96.20 12	99.80 57	96.59 85	99.57 55	99.68 41

agg_prior3		97.87 50	97.42 60	99.23 27	99.29 55	98.23 28	97.92 212	98.72 85	92.38 214	97.59 92	98.64 99	96.09 18	99.79 69	96.59 85	99.57 55	99.68 41

agg_prior2														95.87 106	99.57 55	99.68 41

CDPH-MVS		97.94 47	97.49 56	99.28 20	99.47 31	98.44 14	97.91 215	98.67 103	92.57 201	98.77 32	98.85 79	95.93 27	99.72 86	95.56 118	99.69 38	99.68 41

DP-MVS		96.59 105	95.93 114	98.57 69	99.34 39	96.19 107	98.70 116	98.39 153	89.45 282	94.52 171	99.35 18	91.85 101	99.85 40	92.89 191	98.88 94	99.68 41

MP-MVS-pluss		98.31 39	97.92 42	99.49 4	99.72 11	98.88 4	98.43 156	98.78 70	94.10 138	97.69 85	99.42 5	95.25 45	99.92 13	98.09 24	99.80 9	99.67 46
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
MG-MVS		97.81 53	97.60 49	98.44 80	99.12 80	95.97 115	97.75 232	98.78 70	96.89 42	98.46 45	99.22 31	93.90 73	99.68 95	94.81 137	99.52 66	99.67 46

agg_prior1		97.95 46	97.51 55	99.28 20	99.30 52	98.38 17	97.81 227	98.72 85	93.16 182	97.57 93	98.66 97	96.14 15	99.81 50	96.63 84	99.56 61	99.66 48

HPM-MVS++		98.58 18	98.25 29	99.55 1	99.50 27	99.08 1	98.72 112	98.66 106	97.51 8	98.15 55	98.83 81	95.70 33	99.92 13	97.53 52	99.67 39	99.66 48

UA-Net		97.96 45	97.62 48	98.98 49	98.86 96	97.47 60	98.89 69	99.08 20	96.67 49	98.72 35	99.54 1	93.15 79	99.81 50	94.87 134	98.83 98	99.65 50

test_prior3		98.22 42	97.90 43	99.19 28	99.31 47	98.22 30	97.80 228	98.84 54	96.12 63	97.89 75	98.69 92	95.96 25	99.70 91	96.89 71	99.60 49	99.65 50

test_prior				99.19 28	99.31 47	98.22 30		98.84 54					99.70 91			99.65 50

SD-MVS		98.64 10	98.68 3	98.53 73	99.33 42	98.36 21	98.90 65	98.85 53	97.28 21	99.72 1	99.39 7	96.63 7	97.60 283	98.17 23	99.85 2	99.64 53

3Dnovator	94.51 5	97.46 67	96.93 78	99.07 43	97.78 160	97.64 53	99.35 10	99.06 21	97.02 39	93.75 216	99.16 42	89.25 139	99.92 13	97.22 59	99.75 29	99.64 53

test12				99.18 32	99.16 76	98.19 32		98.53 127		98.07 59		95.13 49	99.72 86		99.56 61	99.63 55

旧先验1					99.29 55	97.48 59		98.70 92			99.09 52	95.56 35			99.47 69	99.61 56

test222					99.23 70	97.17 72	97.40 252	98.66 106	88.68 289	98.05 60	98.96 69	94.14 69			99.53 65	99.61 56

1121		97.37 77	96.77 87	99.16 35	99.34 39	97.99 44	98.19 184	98.68 96	90.14 263	98.01 66	98.97 65	94.80 56	99.87 35	93.36 172	99.46 72	99.61 56

无先验							97.58 244	98.72 85	91.38 239				99.87 35	93.36 172		99.60 59

CVMVSNet		95.43 150	96.04 111	93.57 290	97.93 152	83.62 312	98.12 193	98.59 114	95.68 75	96.56 126	99.02 58	87.51 201	97.51 286	93.56 169	97.44 145	99.60 59

新几何1				99.16 35	99.34 39	98.01 41		98.69 93	90.06 265	98.13 56	98.95 71	94.60 58	99.89 27	91.97 213	99.47 69	99.59 61

PHI-MVS		98.34 36	98.06 37	99.18 32	99.15 78	98.12 37	99.04 51	99.09 19	93.32 177	98.83 29	99.10 48	96.54 8	99.83 43	97.70 43	99.76 23	99.59 61

testdata				98.26 89	99.20 74	95.36 143		98.68 96	91.89 225	98.60 41	99.10 48	94.44 65	99.82 48	94.27 151	99.44 74	99.58 63

Test_1112_low_res		96.34 114	95.66 127	98.36 85	98.56 118	95.94 119	97.71 234	98.07 212	92.10 221	94.79 166	97.29 205	91.75 102	99.56 115	94.17 153	96.50 163	99.58 63

1112_ss		96.63 102	96.00 113	98.50 75	98.56 118	96.37 100	98.18 188	98.10 207	92.92 190	94.84 162	98.43 115	92.14 94	99.58 112	94.35 148	96.51 162	99.56 65

PAPM_NR		97.46 67	97.11 71	98.50 75	99.50 27	96.41 99	98.63 128	98.60 113	95.18 102	97.06 105	98.06 147	94.26 68	99.57 113	93.80 163	98.87 96	99.52 66

CSCG		97.85 52	97.74 46	98.20 92	99.67 18	95.16 150	99.22 27	99.32 7	93.04 185	97.02 107	98.92 75	95.36 41	99.91 22	97.43 54	99.64 45	99.52 66

DeepC-MVS	95.98 3	97.88 49	97.58 50	98.77 59	99.25 64	96.93 78	98.83 80	98.75 77	96.96 41	96.89 114	99.50 3	90.46 124	99.87 35	97.84 36	99.76 23	99.52 66
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CANet		98.05 43	97.76 45	98.90 55	98.73 104	97.27 66	98.35 163	98.78 70	97.37 19	97.72 83	98.96 69	91.53 110	99.92 13	98.79 3	99.65 43	99.51 69

TSAR-MVS + GP.		98.38 32	98.24 31	98.81 58	99.22 71	97.25 69	98.11 195	98.29 166	97.19 30	98.99 20	99.02 58	96.22 11	99.67 96	98.52 14	98.56 110	99.51 69

原ACMM1				98.65 65	99.32 45	96.62 89		98.67 103	93.27 180	97.81 77	98.97 65	95.18 47	99.83 43	93.84 161	99.46 72	99.50 71

VNet		97.79 54	97.40 61	98.96 51	98.88 94	97.55 57	98.63 128	98.93 36	96.74 46	99.02 16	98.84 80	90.33 127	99.83 43	98.53 10	96.66 156	99.50 71

EPNet		97.28 80	96.87 81	98.51 74	94.98 298	96.14 108	98.90 65	97.02 282	98.28 1	95.99 149	99.11 46	91.36 111	99.89 27	96.98 64	99.19 85	99.50 71
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		97.70 57	97.46 58	98.44 80	99.27 61	95.91 125	98.63 128	99.16 17	94.48 131	97.67 86	98.88 77	92.80 82	99.91 22	97.11 62	99.12 87	99.50 71

MVS_111021_LR		98.34 36	98.23 32	98.67 64	99.27 61	96.90 80	97.95 210	99.58 3	97.14 33	98.44 49	99.01 62	95.03 51	99.62 105	97.91 29	99.75 29	99.50 71

WTY-MVS		97.37 77	96.92 79	98.72 61	98.86 96	96.89 82	98.31 170	98.71 90	95.26 98	97.67 86	98.56 106	92.21 92	99.78 74	95.89 104	96.85 153	99.48 76

MSLP-MVS++		98.56 21	98.57 5	98.55 71	99.26 63	96.80 83	98.71 113	99.05 23	97.28 21	98.84 27	99.28 25	96.47 9	99.40 129	98.52 14	99.70 37	99.47 77

114514_t		96.93 93	96.27 104	98.92 53	99.50 27	97.63 54	98.85 76	98.90 42	84.80 310	97.77 78	99.11 46	92.84 81	99.66 97	94.85 135	99.77 17	99.47 77

IS-MVSNet		97.22 82	96.88 80	98.25 90	98.85 98	96.36 101	99.19 33	97.97 219	95.39 88	97.23 98	98.99 64	91.11 115	98.93 176	94.60 141	98.59 108	99.47 77

PAPR		96.84 97	96.24 106	98.65 65	98.72 106	96.92 79	97.36 258	98.57 120	93.33 176	96.67 123	97.57 189	94.30 67	99.56 115	91.05 232	98.59 108	99.47 77

LFMVS		95.86 129	94.98 150	98.47 78	98.87 95	96.32 103	98.84 79	96.02 309	93.40 174	98.62 39	99.20 35	74.99 308	99.63 103	97.72 42	97.20 148	99.46 81

Vis-MVSNet (Re-imp)		96.87 96	96.55 95	97.83 113	98.73 104	95.46 140	99.20 31	98.30 164	94.96 113	96.60 125	98.87 78	90.05 131	98.59 205	93.67 166	98.60 107	99.46 81

Vis-MVSNet		97.42 73	97.11 71	98.34 86	98.66 111	96.23 106	99.22 27	99.00 26	96.63 51	98.04 62	99.21 32	88.05 185	99.35 133	96.01 102	99.21 84	99.45 83
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS_0304		97.70 57	97.25 65	99.07 43	98.90 91	97.83 48	98.20 180	98.74 78	97.51 8	98.03 63	99.06 56	86.12 223	99.93 9	99.02 1	99.64 45	99.44 84

DELS-MVS		98.40 31	98.20 34	98.99 47	99.00 86	97.66 52	97.75 232	98.89 44	97.71 6	98.33 53	98.97 65	94.97 52	99.88 34	98.42 16	99.76 23	99.42 85
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
sss		97.39 75	96.98 77	98.61 67	98.60 117	96.61 91	98.22 178	98.93 36	93.97 145	98.01 66	98.48 112	91.98 99	99.85 40	96.45 91	98.15 126	99.39 86

EPP-MVSNet		97.46 67	97.28 64	97.99 106	98.64 113	95.38 142	99.33 13	98.31 161	93.61 168	97.19 99	99.07 55	94.05 70	99.23 139	96.89 71	98.43 117	99.37 87

MVSFormer		97.57 64	97.49 56	97.84 112	98.07 143	95.76 130	99.47 2	98.40 151	94.98 111	98.79 30	98.83 81	92.34 86	98.41 240	96.91 69	99.59 52	99.34 88

jason		97.32 79	97.08 73	98.06 104	97.45 182	95.59 133	97.87 222	97.91 222	94.79 118	98.55 43	98.83 81	91.12 114	99.23 139	97.58 47	99.60 49	99.34 88
jason: jason.
QAPM		96.29 116	95.40 129	98.96 51	97.85 157	97.60 56	99.23 21	98.93 36	89.76 274	93.11 234	99.02 58	89.11 143	99.93 9	91.99 212	99.62 47	99.34 88

mvs_anonymous		96.70 101	96.53 97	97.18 148	98.19 136	93.78 220	98.31 170	98.19 181	94.01 141	94.47 173	98.27 134	92.08 97	98.46 225	97.39 56	97.91 132	99.31 91

lupinMVS		97.44 71	97.22 68	98.12 98	98.07 143	95.76 130	97.68 237	97.76 226	94.50 129	98.79 30	98.61 100	92.34 86	99.30 134	97.58 47	99.59 52	99.31 91

CDS-MVSNet		96.99 91	96.69 89	97.90 110	98.05 146	95.98 111	98.20 180	98.33 160	93.67 166	96.95 108	98.49 111	93.54 74	98.42 233	95.24 130	97.74 141	99.31 91
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Patchmatch-RL test		91.49 271	90.85 263	93.41 291	91.37 316	84.40 310	92.81 325	95.93 313	91.87 227	87.25 290	94.87 291	88.99 145	96.53 310	92.54 200	82.00 307	99.30 94

BH-RMVSNet		95.92 127	95.32 137	97.69 123	98.32 128	94.64 191	98.19 184	97.45 255	94.56 126	96.03 147	98.61 100	85.02 239	99.12 148	90.68 236	99.06 88	99.30 94

Patchmatch-test		94.42 210	93.68 219	96.63 187	97.60 169	91.76 253	94.83 317	97.49 252	89.45 282	94.14 201	97.10 214	88.99 145	98.83 189	85.37 300	98.13 127	99.29 96

TAMVS		97.02 90	96.79 84	97.70 122	98.06 145	95.31 147	98.52 144	98.31 161	93.95 146	97.05 106	98.61 100	93.49 75	98.52 216	95.33 124	97.81 137	99.29 96

PVSNet_Blended		97.38 76	97.12 70	98.14 95	99.25 64	95.35 145	97.28 264	99.26 8	93.13 183	97.94 71	98.21 138	92.74 83	99.81 50	96.88 74	99.40 78	99.27 98

PatchmatchNet		95.71 135	95.52 128	96.29 220	97.58 171	90.72 267	96.84 285	97.52 240	94.06 139	97.08 102	96.96 234	89.24 140	98.90 181	92.03 211	98.37 118	99.26 99
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CHOSEN 280x420		97.18 84	97.18 69	97.20 146	98.81 100	93.27 232	95.78 307	99.15 18	95.25 99	96.79 121	98.11 144	92.29 88	99.07 158	98.56 9	99.85 2	99.25 100

Patchmatch-test1		95.32 160	94.97 152	96.35 215	97.67 164	91.29 260	97.33 261	97.60 232	94.68 120	96.92 112	96.95 235	83.97 261	98.50 219	91.33 227	98.32 121	99.25 100

PLC	95.07 4	97.20 83	96.78 85	98.44 80	99.29 55	96.31 105	98.14 190	98.76 74	92.41 212	96.39 140	98.31 130	94.92 53	99.78 74	94.06 156	98.77 101	99.23 102
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LCM-MVSNet-Re		95.22 164	95.32 137	94.91 267	98.18 138	87.85 303	98.75 103	95.66 315	95.11 105	88.96 284	96.85 245	90.26 129	97.65 281	95.65 116	98.44 115	99.22 103

Effi-MVS+		97.12 87	96.69 89	98.39 84	98.19 136	96.72 87	97.37 256	98.43 148	93.71 159	97.65 89	98.02 149	92.20 93	99.25 137	96.87 77	97.79 138	99.19 104

alignmvs		97.56 65	97.07 74	99.01 46	98.66 111	98.37 20	98.83 80	98.06 214	96.74 46	98.00 68	97.65 182	90.80 121	99.48 127	98.37 19	96.56 160	99.19 104

DP-MVS Recon		97.86 51	97.46 58	99.06 45	99.53 25	98.35 22	98.33 165	98.89 44	92.62 198	98.05 60	98.94 72	95.34 42	99.65 98	96.04 100	99.42 75	99.19 104

OMC-MVS		97.55 66	97.34 62	98.20 92	99.33 42	95.92 123	98.28 174	98.59 114	95.52 83	97.97 69	99.10 48	93.28 78	99.49 123	95.09 132	98.88 94	99.19 104

MDTV_nov1_ep13_2view						84.26 311	96.89 282		90.97 253	97.90 74		89.89 132		93.91 159		99.18 108

MVS_Test		97.28 80	97.00 76	98.13 97	98.33 127	95.97 115	98.74 107	98.07 212	94.27 135	98.44 49	98.07 146	92.48 85	99.26 136	96.43 92	98.19 125	99.16 109

ab-mvs		96.42 111	95.71 123	98.55 71	98.63 114	96.75 86	97.88 221	98.74 78	93.84 151	96.54 130	98.18 140	85.34 236	99.75 83	95.93 103	96.35 170	99.15 110

PVSNet	91.96 18	96.35 113	96.15 108	96.96 161	99.17 75	92.05 248	96.08 299	98.68 96	93.69 162	97.75 80	97.80 171	88.86 153	99.69 94	94.26 152	99.01 89	99.15 110

tpm		94.13 227	93.80 209	95.12 263	96.50 234	87.91 302	97.44 249	95.89 314	92.62 198	96.37 141	96.30 265	84.13 259	98.30 253	93.24 175	91.66 239	99.14 112

F-COLMAP		97.09 89	96.80 82	97.97 107	99.45 33	94.95 162	98.55 141	98.62 112	93.02 186	96.17 144	98.58 105	94.01 71	99.81 50	93.95 158	98.90 93	99.14 112

PMMVS		96.60 103	96.33 102	97.41 140	97.90 154	93.93 216	97.35 259	98.41 149	92.84 194	97.76 79	97.45 195	91.10 116	99.20 141	96.26 95	97.91 132	99.11 114

GA-MVS		94.81 185	94.03 195	97.14 150	97.15 202	93.86 218	96.76 287	97.58 233	94.00 142	94.76 167	97.04 226	80.91 278	98.48 220	91.79 217	96.25 177	99.09 115

EPNet_dtu		95.21 165	94.95 153	95.99 229	96.17 264	90.45 272	98.16 189	97.27 271	96.77 44	93.14 233	98.33 128	90.34 126	98.42 233	85.57 297	98.81 100	99.09 115
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
TAPA-MVS	93.98 7	95.35 157	94.56 168	97.74 118	99.13 79	94.83 177	98.33 165	98.64 111	86.62 298	96.29 142	98.61 100	94.00 72	99.29 135	80.00 310	99.41 76	99.09 115
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
canonicalmvs		97.67 59	97.23 67	98.98 49	98.70 107	98.38 17	99.34 11	98.39 153	96.76 45	97.67 86	97.40 196	92.26 89	99.49 123	98.28 22	96.28 175	99.08 118

VDD-MVS		95.82 131	95.23 141	97.61 133	98.84 99	93.98 215	98.68 121	97.40 260	95.02 110	97.95 70	99.34 19	74.37 313	99.78 74	98.64 4	96.80 154	99.08 118

Test4		92.21 257	90.34 273	97.82 115	92.83 312	95.87 128	97.94 211	98.05 217	94.50 129	82.12 315	94.48 294	59.54 330	98.54 209	95.39 123	98.22 123	99.06 120

ADS-MVSNet2		94.58 203	94.40 176	95.11 264	98.00 147	88.74 291	96.04 300	97.30 268	90.15 261	96.47 137	96.64 254	87.89 189	97.56 285	90.08 245	97.06 149	99.02 121

ADS-MVSNet		95.00 171	94.45 174	96.63 187	98.00 147	91.91 250	96.04 300	97.74 228	90.15 261	96.47 137	96.64 254	87.89 189	98.96 171	90.08 245	97.06 149	99.02 121

CNLPA		97.45 70	97.03 75	98.73 60	99.05 81	97.44 62	98.07 199	98.53 127	95.32 96	96.80 120	98.53 107	93.32 77	99.72 86	94.31 150	99.31 82	99.02 121

test_normal		94.72 192	93.59 223	98.11 99	95.30 295	95.95 118	97.91 215	97.39 262	94.64 124	85.70 299	95.88 278	80.52 283	99.36 132	96.69 82	98.30 122	99.01 124

AdaColmap		97.15 86	96.70 88	98.48 77	99.16 76	96.69 88	98.01 204	98.89 44	94.44 133	96.83 116	98.68 94	90.69 122	99.76 81	94.36 147	99.29 83	98.98 125

Fast-Effi-MVS+		96.28 118	95.70 124	98.03 105	98.29 129	95.97 115	98.58 134	98.25 172	91.74 229	95.29 156	97.23 208	91.03 118	99.15 144	92.90 189	97.96 131	98.97 126

DI_MVS_plusplus_test		94.74 191	93.62 221	98.09 100	95.34 294	95.92 123	98.09 198	97.34 264	94.66 123	85.89 296	95.91 277	80.49 284	99.38 131	96.66 83	98.22 123	98.97 126

EPMVS		94.99 172	94.48 170	96.52 202	97.22 195	91.75 254	97.23 266	91.66 334	94.11 137	97.28 97	96.81 247	85.70 230	98.84 187	93.04 182	97.28 147	98.97 126

LS3D		97.16 85	96.66 92	98.68 63	98.53 121	97.19 71	98.93 63	98.90 42	92.83 195	95.99 149	99.37 12	92.12 95	99.87 35	93.67 166	99.57 55	98.97 126

HY-MVS	93.96 8	96.82 98	96.23 107	98.57 69	98.46 122	97.00 75	98.14 190	98.21 177	93.95 146	96.72 122	97.99 153	91.58 105	99.76 81	94.51 145	96.54 161	98.95 130

MIMVSNet		93.26 246	92.21 250	96.41 211	97.73 163	93.13 237	95.65 308	97.03 281	91.27 248	94.04 206	96.06 274	75.33 306	97.19 291	86.56 290	96.23 178	98.92 131

diffmvs		96.32 115	95.74 118	98.07 103	98.26 130	96.14 108	98.53 143	98.23 175	90.10 264	96.88 115	97.73 174	90.16 130	99.15 144	93.90 160	97.85 136	98.91 132

TESTMET0.1,1		94.18 223	93.69 218	95.63 242	96.92 212	89.12 286	96.91 278	94.78 322	93.17 181	94.88 161	96.45 261	78.52 292	98.92 177	93.09 179	98.50 112	98.85 133

dp		94.15 226	93.90 204	94.90 268	97.31 190	86.82 308	96.97 274	97.19 275	91.22 250	96.02 148	96.61 256	85.51 232	99.02 166	90.00 249	94.30 192	98.85 133

PAPM		94.95 176	94.00 197	97.78 117	97.04 206	95.65 132	96.03 302	98.25 172	91.23 249	94.19 198	97.80 171	91.27 113	98.86 186	82.61 305	97.61 143	98.84 135

VDDNet		95.36 156	94.53 169	97.86 111	98.10 142	95.13 152	98.85 76	97.75 227	90.46 256	98.36 51	99.39 7	73.27 315	99.64 100	97.98 27	96.58 159	98.81 136

CostFormer		94.95 176	94.73 163	95.60 243	97.28 191	89.06 287	97.53 246	96.89 293	89.66 278	96.82 118	96.72 250	86.05 225	98.95 175	95.53 119	96.13 181	98.79 137

UGNet		96.78 99	96.30 103	98.19 94	98.24 131	95.89 127	98.88 71	98.93 36	97.39 16	96.81 119	97.84 165	82.60 270	99.90 25	96.53 88	99.49 67	98.79 137
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
test-LLR		95.10 169	94.87 159	95.80 237	96.77 220	89.70 278	96.91 278	95.21 317	95.11 105	94.83 164	95.72 283	87.71 195	98.97 168	93.06 180	98.50 112	98.72 139

test-mter		94.08 229	93.51 229	95.80 237	96.77 220	89.70 278	96.91 278	95.21 317	92.89 192	94.83 164	95.72 283	77.69 296	98.97 168	93.06 180	98.50 112	98.72 139

DWT-MVSNet_test		94.82 184	94.36 177	96.20 223	97.35 188	90.79 265	98.34 164	96.57 304	92.91 191	95.33 155	96.44 262	82.00 272	99.12 148	94.52 144	95.78 186	98.70 141

MAR-MVS		96.91 94	96.40 100	98.45 79	98.69 109	96.90 80	98.66 126	98.68 96	92.40 213	97.07 104	97.96 154	91.54 109	99.75 83	93.68 165	98.92 92	98.69 142
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
BH-untuned		95.95 125	95.72 120	96.65 184	98.55 120	92.26 245	98.23 177	97.79 225	93.73 157	94.62 168	98.01 151	88.97 149	99.00 167	93.04 182	98.51 111	98.68 143

PCF-MVS	93.45 11	94.68 196	93.43 232	98.42 83	98.62 115	96.77 85	95.48 309	98.20 180	84.63 311	93.34 226	98.32 129	88.55 172	99.81 50	84.80 301	98.96 91	98.68 143
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CANet_DTU		96.96 92	96.55 95	98.21 91	98.17 140	96.07 110	97.98 207	98.21 177	97.24 27	97.13 100	98.93 73	86.88 212	99.91 22	95.00 133	99.37 80	98.66 145

PatchMatch-RL		96.59 105	96.03 112	98.27 88	99.31 47	96.51 95	97.91 215	99.06 21	93.72 158	96.92 112	98.06 147	88.50 175	99.65 98	91.77 218	99.00 90	98.66 145

tpmrst		95.63 139	95.69 125	95.44 250	97.54 174	88.54 296	96.97 274	97.56 234	93.50 171	97.52 95	96.93 241	89.49 133	99.16 143	95.25 129	96.42 165	98.64 147

IB-MVS	91.98 17	93.27 245	91.97 252	97.19 147	97.47 178	93.41 231	97.09 272	95.99 310	93.32 177	92.47 250	95.73 281	78.06 294	99.53 120	94.59 142	82.98 305	98.62 148
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
DSMNet-mixed		92.52 254	92.58 245	92.33 298	94.15 306	82.65 316	98.30 172	94.26 327	89.08 287	92.65 243	95.73 281	85.01 240	95.76 315	86.24 292	97.76 140	98.59 149

tpm2		94.19 221	93.76 214	95.46 248	97.23 194	89.04 288	97.31 263	96.85 296	87.08 297	96.21 143	96.79 248	83.75 266	98.74 195	92.43 203	96.23 178	98.59 149

MSDG		95.93 126	95.30 139	97.83 113	98.90 91	95.36 143	96.83 286	98.37 156	91.32 244	94.43 181	98.73 91	90.27 128	99.60 106	90.05 247	98.82 99	98.52 151

tpmp4_e23		93.91 235	93.42 234	95.38 256	97.62 167	88.59 295	97.52 247	97.34 264	87.94 293	94.17 200	96.79 248	82.91 268	99.05 159	90.62 238	95.91 183	98.50 152

PatchT		93.06 250	91.97 252	96.35 215	96.69 226	92.67 241	94.48 320	97.08 277	86.62 298	97.08 102	92.23 320	87.94 187	97.90 274	78.89 314	96.69 155	98.49 153

CR-MVSNet		94.76 187	94.15 187	96.59 192	97.00 207	93.43 229	94.96 313	97.56 234	92.46 202	96.93 110	96.24 266	88.15 181	97.88 278	87.38 285	96.65 157	98.46 154

RPMNet		92.52 254	91.17 257	96.59 192	97.00 207	93.43 229	94.96 313	97.26 272	82.27 317	96.93 110	92.12 321	86.98 210	97.88 278	76.32 319	96.65 157	98.46 154

PatchFormer-LS_test		95.47 147	95.27 140	96.08 228	97.59 170	90.66 268	98.10 197	97.34 264	93.98 144	96.08 145	96.15 272	87.65 199	99.12 148	95.27 128	95.24 189	98.44 156

view600		95.60 142	94.93 154	97.62 128	99.05 81	94.85 166	99.09 45	97.01 284	95.36 90	96.52 132	97.37 197	84.55 247	99.59 107	89.07 266	96.39 166	98.40 157

view800		95.60 142	94.93 154	97.62 128	99.05 81	94.85 166	99.09 45	97.01 284	95.36 90	96.52 132	97.37 197	84.55 247	99.59 107	89.07 266	96.39 166	98.40 157

conf0.05thres1000		95.60 142	94.93 154	97.62 128	99.05 81	94.85 166	99.09 45	97.01 284	95.36 90	96.52 132	97.37 197	84.55 247	99.59 107	89.07 266	96.39 166	98.40 157

tfpn		95.60 142	94.93 154	97.62 128	99.05 81	94.85 166	99.09 45	97.01 284	95.36 90	96.52 132	97.37 197	84.55 247	99.59 107	89.07 266	96.39 166	98.40 157

thres600view7		95.49 146	94.77 161	97.67 125	98.98 88	95.02 155	98.85 76	96.90 292	95.38 89	96.63 124	96.90 242	84.29 254	99.59 107	88.65 275	96.33 171	98.40 157

thres400		95.38 154	94.62 165	97.65 127	98.94 89	94.98 159	98.68 121	96.93 290	95.33 94	96.55 128	96.53 257	84.23 256	99.56 115	88.11 281	96.29 173	98.40 157

TR-MVS		94.94 178	94.20 184	97.17 149	97.75 161	94.14 212	97.59 243	97.02 282	92.28 219	95.75 151	97.64 184	83.88 263	98.96 171	89.77 251	96.15 180	98.40 157

JIA-IIPM		93.35 242	92.49 246	95.92 231	96.48 236	90.65 269	95.01 312	96.96 288	85.93 304	96.08 145	87.33 325	87.70 197	98.78 194	91.35 226	95.58 187	98.34 164

PVSNet_0	88.72 19	91.28 273	90.03 276	95.00 266	97.99 149	87.29 306	94.84 316	98.50 136	92.06 222	89.86 276	95.19 287	79.81 287	99.39 130	92.27 204	69.79 329	98.33 165

1314		96.25 120	95.73 119	97.79 116	97.13 203	95.55 138	98.19 184	98.59 114	93.47 172	92.03 259	97.82 169	91.33 112	99.49 123	94.62 140	98.44 115	98.32 166

RPSCF		94.87 180	95.40 129	93.26 294	98.89 93	82.06 318	98.33 165	98.06 214	90.30 260	96.56 126	99.26 27	87.09 207	99.49 123	93.82 162	96.32 172	98.24 167

tpmvs		94.60 200	94.36 177	95.33 259	97.46 179	88.60 294	96.88 283	97.68 229	91.29 246	93.80 215	96.42 263	88.58 169	99.24 138	91.06 230	96.04 182	98.17 168

BH-w/o		95.38 154	95.08 146	96.26 221	98.34 126	91.79 252	97.70 235	97.43 257	92.87 193	94.24 195	97.22 209	88.66 168	98.84 187	91.55 222	97.70 142	98.16 169

tpm cat1		93.36 241	92.80 241	95.07 265	97.58 171	87.97 301	96.76 287	97.86 223	82.17 318	93.53 220	96.04 275	86.13 222	99.13 147	89.24 263	95.87 184	98.10 170

MVS		94.67 197	93.54 226	98.08 101	96.88 216	96.56 93	98.19 184	98.50 136	78.05 324	92.69 242	98.02 149	91.07 117	99.63 103	90.09 244	98.36 119	98.04 171

AllTest		95.24 163	94.65 164	96.99 158	99.25 64	93.21 235	98.59 132	98.18 184	91.36 240	93.52 221	98.77 87	84.67 244	99.72 86	89.70 255	97.87 134	98.02 172

TestCases				96.99 158	99.25 64	93.21 235		98.18 184	91.36 240	93.52 221	98.77 87	84.67 244	99.72 86	89.70 255	97.87 134	98.02 172

gg-mvs-nofinetune		92.21 257	90.58 271	97.13 151	96.75 223	95.09 153	95.85 305	89.40 337	85.43 307	94.50 172	81.98 329	80.80 281	98.40 246	92.16 205	98.33 120	97.88 174

mvs-test1		96.60 103	96.68 91	96.37 213	97.89 155	91.81 251	98.56 139	98.10 207	96.57 52	96.52 132	97.94 156	90.81 119	99.45 128	95.72 111	98.01 129	97.86 175

tfpn200view9		95.32 160	94.62 165	97.43 139	98.94 89	94.98 159	98.68 121	96.93 290	95.33 94	96.55 128	96.53 257	84.23 256	99.56 115	88.11 281	96.29 173	97.76 176

XVG-OURS-SEG-HR		96.51 108	96.34 101	97.02 157	98.77 102	93.76 221	97.79 230	98.50 136	95.45 85	96.94 109	99.09 52	87.87 191	99.55 119	96.76 80	95.83 185	97.74 177

OpenMVS	93.04 13	95.83 130	95.00 148	98.32 87	97.18 200	97.32 64	99.21 30	98.97 29	89.96 267	91.14 265	99.05 57	86.64 215	99.92 13	93.38 171	99.47 69	97.73 178

testgi		93.06 250	92.45 247	94.88 269	96.43 238	89.90 275	98.75 103	97.54 239	95.60 79	91.63 262	97.91 158	74.46 312	97.02 293	86.10 293	93.67 209	97.72 179

XVG-OURS		96.55 107	96.41 99	96.99 158	98.75 103	93.76 221	97.50 248	98.52 129	95.67 76	96.83 116	99.30 24	88.95 150	99.53 120	95.88 105	96.26 176	97.69 180

cascas		94.63 199	93.86 206	96.93 164	96.91 214	94.27 209	96.00 303	98.51 131	85.55 306	94.54 170	96.23 268	84.20 258	98.87 184	95.80 109	96.98 152	97.66 181

test0.0.03 1		94.08 229	93.51 229	95.80 237	95.53 290	92.89 240	97.38 254	95.97 311	95.11 105	92.51 249	96.66 252	87.71 195	96.94 294	87.03 288	93.67 209	97.57 182

LP		91.12 275	89.99 277	94.53 278	96.35 246	88.70 292	93.86 324	97.35 263	84.88 309	90.98 267	94.77 292	84.40 253	97.43 287	75.41 322	91.89 236	97.47 183

MVS-HIRNet		89.46 287	88.40 289	92.64 296	97.58 171	82.15 317	94.16 323	93.05 333	75.73 326	90.90 268	82.52 328	79.42 289	98.33 248	83.53 303	98.68 102	97.43 184

xiu_mvs_v2_base		97.66 60	97.70 47	97.56 135	98.61 116	95.46 140	97.44 249	98.46 141	97.15 32	98.65 38	98.15 141	94.33 66	99.80 57	97.84 36	98.66 106	97.41 185

Effi-MVS+-dtu		96.29 116	96.56 94	95.51 244	97.89 155	90.22 274	98.80 91	98.10 207	96.57 52	96.45 139	96.66 252	90.81 119	98.91 178	95.72 111	97.99 130	97.40 186

PS-MVSNAJ		97.73 55	97.77 44	97.62 128	98.68 110	95.58 134	97.34 260	98.51 131	97.29 20	98.66 37	97.88 161	94.51 60	99.90 25	97.87 33	99.17 86	97.39 187

thres200		95.25 162	94.57 167	97.28 144	98.81 100	94.92 163	98.20 180	97.11 276	95.24 101	96.54 130	96.22 270	84.58 246	99.53 120	87.93 283	96.50 163	97.39 187

xiu_mvs_v1_base_debu		97.60 61	97.56 51	97.72 119	98.35 123	95.98 111	97.86 223	98.51 131	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 189

xiu_mvs_v1_base		97.60 61	97.56 51	97.72 119	98.35 123	95.98 111	97.86 223	98.51 131	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 189

xiu_mvs_v1_base_debi		97.60 61	97.56 51	97.72 119	98.35 123	95.98 111	97.86 223	98.51 131	97.13 34	99.01 17	98.40 117	91.56 106	99.80 57	98.53 10	98.68 102	97.37 189

API-MVS		97.41 74	97.25 65	97.91 109	98.70 107	96.80 83	98.82 82	98.69 93	94.53 127	98.11 57	98.28 131	94.50 63	99.57 113	94.12 155	99.49 67	97.37 189

Fast-Effi-MVS+-dtu		95.87 128	95.85 116	95.91 232	97.74 162	91.74 255	98.69 117	98.15 192	95.56 81	94.92 160	97.68 181	88.98 148	98.79 193	93.19 177	97.78 139	97.20 193

COLMAP_ROB	93.27 12	95.33 159	94.87 159	96.71 173	99.29 55	93.24 234	98.58 134	98.11 202	89.92 270	93.57 219	99.10 48	86.37 219	99.79 69	90.78 234	98.10 128	97.09 194
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
pcd1.5k->3k		39.42 316	41.78 317	32.35 329	96.17 264	0.00 347	0.00 338	98.54 124	0.00 342	0.00 343	0.00 344	87.78 194	0.00 345	0.00 342	93.56 213	97.06 195

PS-MVSNAJss		96.43 110	96.26 105	96.92 166	95.84 280	95.08 154	99.16 35	98.50 136	95.87 70	93.84 214	98.34 127	94.51 60	98.61 202	96.88 74	93.45 216	97.06 195

nrg030		96.28 118	95.72 120	97.96 108	96.90 215	98.15 35	99.39 5	98.31 161	95.47 84	94.42 182	98.35 123	92.09 96	98.69 196	97.50 53	89.05 261	97.04 197

FIs		96.51 108	96.12 109	97.67 125	97.13 203	97.54 58	99.36 8	99.22 14	95.89 69	94.03 207	98.35 123	91.98 99	98.44 230	96.40 93	92.76 226	97.01 198

FC-MVSNet-test		96.42 111	96.05 110	97.53 136	96.95 210	97.27 66	99.36 8	99.23 12	95.83 71	93.93 209	98.37 121	92.00 98	98.32 249	96.02 101	92.72 227	97.00 199

EU-MVSNet		93.66 238	94.14 188	92.25 299	95.96 274	83.38 313	98.52 144	98.12 197	94.69 119	92.61 244	98.13 143	87.36 205	96.39 312	91.82 216	90.00 249	96.98 200

VPNet		94.99 172	94.19 185	97.40 141	97.16 201	96.57 92	98.71 113	98.97 29	95.67 76	94.84 162	98.24 137	80.36 285	98.67 199	96.46 90	87.32 286	96.96 201

XXY-MVS		95.20 166	94.45 174	97.46 137	96.75 223	96.56 93	98.86 75	98.65 110	93.30 179	93.27 227	98.27 134	84.85 243	98.87 184	94.82 136	91.26 243	96.96 201

TranMVSNet+NR-MVSNet		95.14 168	94.48 170	97.11 153	96.45 237	96.36 101	99.03 52	99.03 24	95.04 109	93.58 218	97.93 157	88.27 178	98.03 267	94.13 154	86.90 293	96.95 203

HQP_MVS		96.14 121	95.90 115	96.85 167	97.42 183	94.60 197	98.80 91	98.56 121	97.28 21	95.34 153	98.28 131	87.09 207	99.03 164	96.07 97	94.27 193	96.92 204

plane_prior5								98.56 121					99.03 164	96.07 97	94.27 193	96.92 204

UniMVSNet_NR-MVSNet		95.71 135	95.15 144	97.40 141	96.84 218	96.97 76	98.74 107	99.24 10	95.16 103	93.88 211	97.72 177	91.68 103	98.31 251	95.81 107	87.25 288	96.92 204

DU-MVS		95.42 151	94.76 162	97.40 141	96.53 232	96.97 76	98.66 126	98.99 28	95.43 86	93.88 211	97.69 178	88.57 170	98.31 251	95.81 107	87.25 288	96.92 204

NR-MVSNet		94.98 174	94.16 186	97.44 138	96.53 232	97.22 70	98.74 107	98.95 33	94.96 113	89.25 282	97.69 178	89.32 137	98.18 259	94.59 142	87.40 285	96.92 204

jajsoiax		95.45 149	95.03 147	96.73 172	95.42 293	94.63 192	99.14 37	98.52 129	95.74 73	93.22 228	98.36 122	83.87 264	98.65 200	96.95 68	94.04 202	96.91 209

mvs_tets		95.41 153	95.00 148	96.65 184	95.58 288	94.42 202	99.00 54	98.55 123	95.73 74	93.21 229	98.38 120	83.45 267	98.63 201	97.09 63	94.00 204	96.91 209

WR-MVS		95.15 167	94.46 172	97.22 145	96.67 228	96.45 97	98.21 179	98.81 60	94.15 136	93.16 230	97.69 178	87.51 201	98.30 253	95.29 127	88.62 272	96.90 211

VPA-MVSNet		95.75 133	95.11 145	97.69 123	97.24 193	97.27 66	98.94 62	99.23 12	95.13 104	95.51 152	97.32 203	85.73 229	98.91 178	97.33 58	89.55 255	96.89 212

test_djsdf		96.00 123	95.69 125	96.93 164	95.72 284	95.49 139	99.47 2	98.40 151	94.98 111	94.58 169	97.86 162	89.16 142	98.41 240	96.91 69	94.12 201	96.88 213

HQP4-MVS										94.45 174			98.96 171			96.87 214

ACMM	93.85 9	95.69 137	95.38 133	96.61 190	97.61 168	93.84 219	98.91 64	98.44 145	95.25 99	94.28 192	98.47 113	86.04 227	99.12 148	95.50 120	93.95 206	96.87 214
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
HQP-MVS		95.72 134	95.40 129	96.69 176	97.20 197	94.25 210	98.05 200	98.46 141	96.43 54	94.45 174	97.73 174	86.75 213	98.96 171	95.30 125	94.18 197	96.86 216

testing_2		90.61 281	88.50 288	96.95 162	90.08 320	95.57 135	97.69 236	98.06 214	93.02 186	76.55 322	92.48 318	61.18 329	98.44 230	95.45 122	91.98 233	96.84 217

EI-MVSNet		95.96 124	95.83 117	96.36 214	97.93 152	93.70 225	98.12 193	98.27 167	93.70 161	95.07 157	99.02 58	92.23 91	98.54 209	94.68 138	93.46 214	96.84 217

IterMVS-LS		95.46 148	95.21 142	96.22 222	98.12 141	93.72 224	98.32 169	98.13 195	93.71 159	94.26 193	97.31 204	92.24 90	98.10 262	94.63 139	90.12 247	96.84 217
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
CP-MVSNet		94.94 178	94.30 179	96.83 168	96.72 225	95.56 136	99.11 43	98.95 33	93.89 148	92.42 252	97.90 159	87.19 206	98.12 261	94.32 149	88.21 275	96.82 220

PS-CasMVS		94.67 197	93.99 199	96.71 173	96.68 227	95.26 148	99.13 40	99.03 24	93.68 164	92.33 253	97.95 155	85.35 235	98.10 262	93.59 168	88.16 277	96.79 221

UniMVSNet (Re)		95.78 132	95.19 143	97.58 134	96.99 209	97.47 60	98.79 96	99.18 16	95.60 79	93.92 210	97.04 226	91.68 103	98.48 220	95.80 109	87.66 283	96.79 221

MVSTER		96.06 122	95.72 120	97.08 155	98.23 132	95.93 122	98.73 110	98.27 167	94.86 117	95.07 157	98.09 145	88.21 179	98.54 209	96.59 85	93.46 214	96.79 221

LPG-MVS_test		95.62 140	95.34 134	96.47 206	97.46 179	93.54 226	98.99 55	98.54 124	94.67 121	94.36 184	98.77 87	85.39 233	99.11 152	95.71 113	94.15 199	96.76 224

LGP-MVS_train				96.47 206	97.46 179	93.54 226		98.54 124	94.67 121	94.36 184	98.77 87	85.39 233	99.11 152	95.71 113	94.15 199	96.76 224

GG-mvs-BLEND				96.59 192	96.34 247	94.98 159	96.51 297	88.58 338		93.10 235	94.34 297	80.34 286	98.05 266	89.53 258	96.99 151	96.74 226

PEN-MVS		94.42 210	93.73 216	96.49 204	96.28 257	94.84 175	99.17 34	99.00 26	93.51 170	92.23 255	97.83 168	86.10 224	97.90 274	92.55 199	86.92 292	96.74 226

OurMVSNet-221017-0		94.21 219	94.00 197	94.85 270	95.60 287	89.22 285	98.89 69	97.43 257	95.29 97	92.18 257	98.52 110	82.86 269	98.59 205	93.46 170	91.76 237	96.74 226

v2v482		94.69 193	94.03 195	96.65 184	96.17 264	94.79 185	98.67 124	98.08 211	92.72 196	94.00 208	97.16 211	87.69 198	98.45 227	92.91 188	88.87 267	96.72 229

GBi-Net		94.49 206	93.80 209	96.56 197	98.21 133	95.00 156	98.82 82	98.18 184	92.46 202	94.09 203	97.07 218	81.16 275	97.95 271	92.08 207	92.14 230	96.72 229

test1		94.49 206	93.80 209	96.56 197	98.21 133	95.00 156	98.82 82	98.18 184	92.46 202	94.09 203	97.07 218	81.16 275	97.95 271	92.08 207	92.14 230	96.72 229

FMVSNet1		93.19 248	92.07 251	96.56 197	97.54 174	95.00 156	98.82 82	98.18 184	90.38 259	92.27 254	97.07 218	73.68 314	97.95 271	89.36 262	91.30 241	96.72 229

v1192		94.32 214	93.58 224	96.53 201	96.10 268	94.45 201	98.50 149	98.17 189	91.54 233	94.19 198	97.06 221	86.95 211	98.43 232	90.14 243	89.57 253	96.70 233

v1240		94.06 231	93.29 235	96.34 217	96.03 272	93.90 217	98.44 154	98.17 189	91.18 251	94.13 202	97.01 230	86.05 225	98.42 233	89.13 265	89.50 256	96.70 233

FMVSNet3		94.97 175	94.26 180	97.11 153	98.18 138	96.62 89	98.56 139	98.26 171	93.67 166	94.09 203	97.10 214	84.25 255	98.01 268	92.08 207	92.14 230	96.70 233

FMVSNet2		94.47 208	93.61 222	97.04 156	98.21 133	96.43 98	98.79 96	98.27 167	92.46 202	93.50 223	97.09 216	81.16 275	98.00 269	91.09 228	91.93 234	96.70 233

ACMH	92.88 16	94.55 204	93.95 201	96.34 217	97.63 166	93.26 233	98.81 88	98.49 140	93.43 173	89.74 277	98.53 107	81.91 273	99.08 157	93.69 164	93.30 220	96.70 233
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
v1921920		94.20 220	93.47 231	96.40 212	95.98 273	94.08 213	98.52 144	98.15 192	91.33 243	94.25 194	97.20 210	86.41 218	98.42 233	90.04 248	89.39 258	96.69 238

v1141		94.75 189	94.11 192	96.67 182	96.27 259	94.86 165	98.69 117	98.12 197	92.43 210	94.31 188	96.94 237	88.78 163	98.48 220	92.63 196	88.85 269	96.67 239

divwei89l23v2f112		94.76 187	94.12 191	96.67 182	96.28 257	94.85 166	98.69 117	98.12 197	92.44 209	94.29 191	96.94 237	88.85 155	98.48 220	92.67 194	88.79 271	96.67 239

v1		94.75 189	94.11 192	96.69 176	96.27 259	94.87 164	98.69 117	98.12 197	92.43 210	94.32 187	96.94 237	88.71 167	98.54 209	92.66 195	88.84 270	96.67 239

ACMP	93.49 10	95.34 158	94.98 150	96.43 210	97.67 164	93.48 228	98.73 110	98.44 145	94.94 116	92.53 247	98.53 107	84.50 252	99.14 146	95.48 121	94.00 204	96.66 242
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CLD-MVS		95.62 140	95.34 134	96.46 209	97.52 176	93.75 223	97.27 265	98.46 141	95.53 82	94.42 182	98.00 152	86.21 221	98.97 168	96.25 96	94.37 191	96.66 242
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
v1neww		94.83 181	94.22 181	96.68 179	96.39 240	94.85 166	98.87 72	98.11 202	92.45 207	94.45 174	97.06 221	88.82 158	98.54 209	92.93 186	88.91 265	96.65 244

v7new		94.83 181	94.22 181	96.68 179	96.39 240	94.85 166	98.87 72	98.11 202	92.45 207	94.45 174	97.06 221	88.82 158	98.54 209	92.93 186	88.91 265	96.65 244

v6		94.83 181	94.21 183	96.69 176	96.36 244	94.85 166	98.87 72	98.11 202	92.46 202	94.44 180	97.05 225	88.76 164	98.57 207	92.95 185	88.92 264	96.65 244

v144192		94.39 212	93.70 217	96.48 205	96.06 270	94.35 206	98.58 134	98.16 191	91.45 235	94.33 186	97.02 228	87.50 203	98.45 227	91.08 229	89.11 260	96.63 247

IterMVS		94.09 228	93.85 207	94.80 273	97.99 149	90.35 273	97.18 269	98.12 197	93.68 164	92.46 251	97.34 201	84.05 260	97.41 288	92.51 201	91.33 240	96.62 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1144		94.59 202	93.92 202	96.60 191	96.21 261	94.78 187	98.59 132	98.14 194	91.86 228	94.21 197	97.02 228	87.97 186	98.41 240	91.72 219	89.57 253	96.61 249

v7		94.69 193	94.04 194	96.62 189	96.41 239	94.79 185	98.78 98	98.13 195	91.89 225	94.30 190	97.16 211	88.13 183	98.45 227	91.96 214	89.65 252	96.61 249

OPM-MVS		95.69 137	95.33 136	96.76 171	96.16 267	94.63 192	98.43 156	98.39 153	96.64 50	95.02 159	98.78 85	85.15 238	99.05 159	95.21 131	94.20 196	96.60 251

LTVRE_ROB	92.95 15	94.60 200	93.90 204	96.68 179	97.41 186	94.42 202	98.52 144	98.59 114	91.69 230	91.21 264	98.35 123	84.87 242	99.04 163	91.06 230	93.44 217	96.60 251
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
semantic-postprocess				94.85 270	97.98 151	90.56 271		98.11 202	93.75 154	92.58 245	97.48 192	83.91 262	97.41 288	92.48 202	91.30 241	96.58 253

pmmvs5		93.65 240	92.97 239	95.68 241	95.49 291	92.37 244	98.20 180	97.28 270	89.66 278	92.58 245	97.26 206	82.14 271	98.09 264	93.18 178	90.95 244	96.58 253

K. test v3		92.55 253	91.91 254	94.48 280	95.64 286	89.24 284	99.07 49	94.88 321	94.04 140	86.78 292	97.59 187	77.64 299	97.64 282	92.08 207	89.43 257	96.57 255

SixPastTwentyTwo		93.34 243	92.86 240	94.75 274	95.67 285	89.41 283	98.75 103	96.67 301	93.89 148	90.15 275	98.25 136	80.87 279	98.27 256	90.90 233	90.64 245	96.57 255

MDA-MVSNet_test_wron		90.71 279	89.38 282	94.68 275	94.83 301	90.78 266	97.19 268	97.46 253	87.60 294	72.41 327	95.72 283	86.51 216	96.71 307	85.92 295	86.80 294	96.56 257

ACMH+	92.99 14	94.30 215	93.77 212	95.88 234	97.81 159	92.04 249	98.71 113	98.37 156	93.99 143	90.60 272	98.47 113	80.86 280	99.05 159	92.75 193	92.40 229	96.55 258

YYNet1		90.70 280	89.39 281	94.62 277	94.79 302	90.65 269	97.20 267	97.46 253	87.54 295	72.54 326	95.74 280	86.51 216	96.66 308	86.00 294	86.76 295	96.54 259

Patchmtry		93.22 247	92.35 248	95.84 235	96.77 220	93.09 238	94.66 319	97.56 234	87.37 296	92.90 238	96.24 266	88.15 181	97.90 274	87.37 286	90.10 248	96.53 260

v7n		94.19 221	93.43 232	96.47 206	95.90 276	94.38 205	99.26 17	98.34 159	91.99 223	92.76 241	97.13 213	88.31 177	98.52 216	89.48 260	87.70 282	96.52 261

MDA-MVSNet-bldmvs		89.97 284	88.35 290	94.83 272	95.21 296	91.34 258	97.64 240	97.51 243	88.36 291	71.17 328	96.13 273	79.22 290	96.63 309	83.65 302	86.27 296	96.52 261

lessismore_v0				94.45 283	94.93 300	88.44 297		91.03 335		86.77 293	97.64 184	76.23 303	98.42 233	90.31 242	85.64 301	96.51 263

anonymousdsp		95.42 151	94.91 158	96.94 163	95.10 297	95.90 126	99.14 37	98.41 149	93.75 154	93.16 230	97.46 193	87.50 203	98.41 240	95.63 117	94.03 203	96.50 264

v148		94.29 216	93.76 214	95.91 232	96.10 268	92.93 239	98.58 134	97.97 219	92.59 200	93.47 224	96.95 235	88.53 173	98.32 249	92.56 198	87.06 290	96.49 265

XVG-ACMP-BASELINE		94.54 205	94.14 188	95.75 240	96.55 231	91.65 256	98.11 195	98.44 145	94.96 113	94.22 196	97.90 159	79.18 291	99.11 152	94.05 157	93.85 207	96.48 266

DTE-MVSNet		93.98 233	93.26 236	96.14 225	96.06 270	94.39 204	99.20 31	98.86 52	93.06 184	91.78 260	97.81 170	85.87 228	97.58 284	90.53 239	86.17 297	96.46 267

v8		94.47 208	93.77 212	96.57 196	96.36 244	94.83 177	99.05 50	98.19 181	91.92 224	93.16 230	96.97 233	88.82 158	98.48 220	91.69 220	87.79 281	96.39 268

WR-MVS_H		95.05 170	94.46 172	96.81 169	96.86 217	95.82 129	99.24 20	99.24 10	93.87 150	92.53 247	96.84 246	90.37 125	98.24 257	93.24 175	87.93 278	96.38 269

v748		93.75 237	93.06 237	95.82 236	95.73 283	92.64 242	99.25 19	98.24 174	91.60 232	92.22 256	96.52 259	87.60 200	98.46 225	90.64 237	85.72 300	96.36 270

V42		94.78 186	94.14 188	96.70 175	96.33 251	95.22 149	98.97 59	98.09 210	92.32 217	94.31 188	97.06 221	88.39 176	98.55 208	92.90 189	88.87 267	96.34 271

v10		94.29 216	93.55 225	96.51 203	96.39 240	94.80 182	98.99 55	98.19 181	91.35 242	93.02 236	96.99 231	88.09 184	98.41 240	90.50 240	88.41 274	96.33 272

pmmvs4		94.69 193	93.99 199	96.81 169	95.74 282	95.94 119	97.40 252	97.67 230	90.42 258	93.37 225	97.59 187	89.08 144	98.20 258	92.97 184	91.67 238	96.30 273

PVSNet_BlendedMVS		96.73 100	96.60 93	97.12 152	99.25 64	95.35 145	98.26 176	99.26 8	94.28 134	97.94 71	97.46 193	92.74 83	99.81 50	96.88 74	93.32 219	96.20 274

pm-mvs1		93.94 234	93.06 237	96.59 192	96.49 235	95.16 150	98.95 61	98.03 218	92.32 217	91.08 266	97.84 165	84.54 251	98.41 240	92.16 205	86.13 299	96.19 275

Anonymous20231206		91.66 270	91.10 258	93.33 292	94.02 308	87.35 305	98.58 134	97.26 272	90.48 255	90.16 274	96.31 264	83.83 265	96.53 310	79.36 312	89.90 250	96.12 276

ITE_SJBPF				95.44 250	97.42 183	91.32 259		97.50 246	95.09 108	93.59 217	98.35 123	81.70 274	98.88 183	89.71 254	93.39 218	96.12 276

FMVSNet5		91.81 268	90.92 262	94.49 279	97.21 196	92.09 247	98.00 206	97.55 238	89.31 285	90.86 269	95.61 286	74.48 311	95.32 317	85.57 297	89.70 251	96.07 278

UnsupCasMVSNet_eth		90.99 277	89.92 278	94.19 286	94.08 307	89.83 276	97.13 271	98.67 103	93.69 162	85.83 298	96.19 271	75.15 307	96.74 304	89.14 264	79.41 316	96.00 279

USDC		93.33 244	92.71 243	95.21 260	96.83 219	90.83 264	96.91 278	97.50 246	93.84 151	90.72 270	98.14 142	77.69 296	98.82 190	89.51 259	93.21 223	95.97 280

pmmvs6		91.77 269	90.63 270	95.17 262	94.69 304	91.24 261	98.67 124	97.92 221	86.14 301	89.62 278	97.56 190	75.79 305	98.34 247	90.75 235	84.56 304	95.94 281

N_pmnet		87.12 295	87.77 292	85.17 315	95.46 292	61.92 337	97.37 256	70.66 345	85.83 305	88.73 286	96.04 275	85.33 237	97.76 280	80.02 309	90.48 246	95.84 282

MIMVSNet1		89.67 286	88.28 291	93.82 288	92.81 313	91.08 263	98.01 204	97.45 255	87.95 292	87.90 289	95.87 279	67.63 324	94.56 320	78.73 315	88.18 276	95.83 283

test2356		88.68 291	88.61 287	88.87 306	89.90 321	78.23 321	95.11 311	96.66 303	88.66 290	89.06 283	94.33 298	73.14 316	92.56 328	75.56 321	95.11 190	95.81 284

test1235678		86.26 297	85.81 296	87.62 309	86.97 326	75.00 328	96.55 295	96.32 308	86.08 303	81.32 318	92.98 310	73.10 317	92.05 329	71.64 325	87.32 286	95.81 284

TransMVSNet (Re)		92.67 252	91.51 256	96.15 224	96.58 230	94.65 190	98.90 65	96.73 297	90.86 254	89.46 280	97.86 162	85.62 231	98.09 264	86.45 291	81.12 310	95.71 286

Baseline_NR-MVSNet		94.35 213	93.81 208	95.96 230	96.20 262	94.05 214	98.61 131	96.67 301	91.44 236	93.85 213	97.60 186	88.57 170	98.14 260	94.39 146	86.93 291	95.68 287

testus		88.91 289	89.08 284	88.40 307	91.39 315	76.05 324	96.56 293	96.48 305	89.38 284	89.39 281	95.17 289	70.94 318	93.56 324	77.04 318	95.41 188	95.61 288

V4		94.18 223	93.52 227	96.13 226	95.89 277	94.31 207	99.23 21	98.22 176	91.42 237	92.82 240	96.89 243	87.93 188	98.52 216	91.51 223	87.81 279	95.58 289

v52		94.18 223	93.52 227	96.13 226	95.95 275	94.29 208	99.23 21	98.21 177	91.42 237	92.84 239	96.89 243	87.85 192	98.53 215	91.51 223	87.81 279	95.57 290

TinyColmap		92.31 256	91.53 255	94.65 276	96.92 212	89.75 277	96.92 276	96.68 300	90.45 257	89.62 278	97.85 164	76.06 304	98.81 191	86.74 289	92.51 228	95.41 291

MS-PatchMatch		93.84 236	93.63 220	94.46 282	96.18 263	89.45 281	97.76 231	98.27 167	92.23 220	92.13 258	97.49 191	79.50 288	98.69 196	89.75 253	99.38 79	95.25 292

LF4IMVS		93.14 249	92.79 242	94.20 285	95.88 278	88.67 293	97.66 239	97.07 278	93.81 153	91.71 261	97.65 182	77.96 295	98.81 191	91.47 225	91.92 235	95.12 293

tfpnnormal		93.66 238	92.70 244	96.55 200	96.94 211	95.94 119	98.97 59	99.19 15	91.04 252	91.38 263	97.34 201	84.94 241	98.61 202	85.45 299	89.02 263	95.11 294

EG-PatchMatch MVS		91.13 274	90.12 275	94.17 287	94.73 303	89.00 289	98.13 192	97.81 224	89.22 286	85.32 301	96.46 260	67.71 323	98.42 233	87.89 284	93.82 208	95.08 295

TDRefinement		91.06 276	89.68 279	95.21 260	85.35 328	91.49 257	98.51 148	97.07 278	91.47 234	88.83 285	97.84 165	77.31 300	99.09 156	92.79 192	77.98 322	95.04 296

MVP-Stereo		94.28 218	93.92 202	95.35 258	94.95 299	92.60 243	97.97 208	97.65 231	91.61 231	90.68 271	97.09 216	86.32 220	98.42 233	89.70 255	99.34 81	95.02 297
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
test20.03		90.89 278	90.38 272	92.43 297	93.48 309	88.14 300	98.33 165	97.56 234	93.40 174	87.96 288	96.71 251	80.69 282	94.13 321	79.15 313	86.17 297	95.01 298

ambc				89.49 305	86.66 327	75.78 325	92.66 326	96.72 298		86.55 294	92.50 317	46.01 334	97.90 274	90.32 241	82.09 306	94.80 299

v18		92.10 259	90.97 259	95.50 245	96.34 247	94.85 166	98.82 82	97.52 240	89.99 266	85.31 303	93.26 302	88.90 152	96.92 295	88.82 271	79.77 314	94.73 300

v16		92.08 260	90.94 260	95.49 246	96.38 243	94.84 175	98.81 88	97.51 243	89.94 269	85.25 304	93.28 301	88.86 153	96.91 296	88.70 273	79.78 313	94.72 301

v17		92.08 260	90.94 260	95.48 247	96.34 247	94.83 177	98.81 88	97.52 240	89.95 268	85.32 301	93.24 303	88.91 151	96.91 296	88.76 272	79.63 315	94.71 302

v13		91.88 266	90.69 268	95.43 252	96.33 251	94.78 187	98.75 103	97.50 246	89.68 277	84.93 310	92.98 310	88.84 156	96.83 300	88.14 280	79.09 318	94.69 303

V9		91.91 264	90.73 266	95.45 249	96.32 254	94.80 182	98.77 99	97.50 246	89.81 273	85.03 308	93.08 306	88.76 164	96.86 298	88.24 278	79.03 320	94.69 303

v12		91.89 265	90.70 267	95.43 252	96.31 255	94.80 182	98.76 102	97.50 246	89.76 274	84.95 309	93.00 309	88.82 158	96.82 302	88.23 279	79.00 321	94.68 305

v15		91.94 262	90.77 264	95.43 252	96.31 255	94.83 177	98.77 99	97.50 246	89.92 270	85.13 305	93.08 306	88.76 164	96.86 298	88.40 276	79.10 317	94.61 306

V14		91.93 263	90.76 265	95.42 255	96.33 251	94.81 181	98.77 99	97.51 243	89.86 272	85.09 306	93.13 304	88.80 162	96.83 300	88.32 277	79.06 319	94.60 307

v11		91.85 267	90.68 269	95.36 257	96.34 247	94.74 189	98.80 91	97.43 257	89.60 280	85.09 306	93.03 308	88.53 173	96.75 303	87.37 286	79.96 312	94.58 308

Anonymous20231211		83.69 299	81.50 301	90.26 303	89.23 322	80.10 320	97.97 208	97.06 280	72.79 328	82.05 316	92.57 316	50.28 332	96.32 313	76.15 320	75.38 326	94.37 309

test_0402		91.32 272	90.27 274	94.48 280	96.60 229	91.12 262	98.50 149	97.22 274	86.10 302	88.30 287	96.98 232	77.65 298	97.99 270	78.13 316	92.94 225	94.34 310

new_pmnet		90.06 283	89.00 286	93.22 295	94.18 305	88.32 299	96.42 298	96.89 293	86.19 300	85.67 300	93.62 299	77.18 301	97.10 292	81.61 307	89.29 259	94.23 311

CMPMVS	66.06 21	89.70 285	89.67 280	89.78 304	93.19 310	76.56 323	97.00 273	98.35 158	80.97 320	81.57 317	97.75 173	74.75 310	98.61 202	89.85 250	93.63 211	94.17 312
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
PM-MVS		87.77 293	86.55 295	91.40 302	91.03 318	83.36 314	96.92 276	95.18 319	91.28 247	86.48 295	93.42 300	53.27 331	96.74 304	89.43 261	81.97 308	94.11 313

pmmvs-eth3d		90.36 282	89.05 285	94.32 284	91.10 317	92.12 246	97.63 242	96.95 289	88.86 288	84.91 311	93.13 304	78.32 293	96.74 304	88.70 273	81.81 309	94.09 314

new-patchmatchnet		88.50 292	87.45 293	91.67 301	90.31 319	85.89 309	97.16 270	97.33 267	89.47 281	83.63 313	92.77 314	76.38 302	95.06 319	82.70 304	77.29 323	94.06 315

pmmvs3		86.67 296	84.86 298	92.11 300	88.16 323	87.19 307	96.63 290	94.75 323	79.88 322	87.22 291	92.75 315	66.56 325	95.20 318	81.24 308	76.56 325	93.96 316

UnsupCasMVSNet_bld		87.17 294	85.12 297	93.31 293	91.94 314	88.77 290	94.92 315	98.30 164	84.30 312	82.30 314	90.04 322	63.96 328	97.25 290	85.85 296	74.47 328	93.93 317

LCM-MVSNet		78.70 302	76.24 306	86.08 312	77.26 338	71.99 331	94.34 321	96.72 298	61.62 332	76.53 323	89.33 323	33.91 341	92.78 327	81.85 306	74.60 327	93.46 318

OpenMVS_ROB	86.42 20	89.00 288	87.43 294	93.69 289	93.08 311	89.42 282	97.91 215	96.89 293	78.58 323	85.86 297	94.69 293	69.48 320	98.29 255	77.13 317	93.29 221	93.36 319

DeepMVS_CX				86.78 311	97.09 205	72.30 330		95.17 320	75.92 325	84.34 312	95.19 287	70.58 319	95.35 316	79.98 311	89.04 262	92.68 320

1111		84.94 298	84.30 299	86.86 310	87.59 324	75.10 326	96.63 290	96.43 306	82.53 315	80.75 319	92.91 312	68.94 321	93.79 322	68.24 328	84.66 303	91.70 321

PMMVS2		77.95 304	75.44 307	85.46 313	82.54 330	74.95 329	94.23 322	93.08 332	72.80 327	74.68 324	87.38 324	36.36 339	91.56 330	73.95 323	63.94 330	89.87 322

test12356		83.47 300	83.37 300	83.78 316	84.43 329	70.09 333	95.12 310	95.60 316	82.98 313	78.89 321	92.43 319	64.99 326	91.41 331	70.36 326	85.55 302	89.82 323

testmv		78.74 301	77.35 302	82.89 318	78.16 337	69.30 334	95.87 304	94.65 324	81.11 319	70.98 329	87.11 326	46.31 333	90.42 332	65.28 331	76.72 324	88.95 324

FPMVS		77.62 305	77.14 303	79.05 320	79.25 334	60.97 338	95.79 306	95.94 312	65.96 329	67.93 330	94.40 295	37.73 338	88.88 334	68.83 327	88.46 273	87.29 325

tmp_tt		68.90 309	66.97 309	74.68 324	50.78 343	59.95 339	87.13 331	83.47 343	38.80 338	62.21 332	96.23 268	64.70 327	76.91 341	88.91 270	30.49 338	87.19 326

ANet_high		69.08 308	65.37 310	80.22 319	65.99 341	71.96 332	90.91 329	90.09 336	82.62 314	49.93 337	78.39 333	29.36 342	81.75 337	62.49 334	38.52 336	86.95 327

no-one		74.41 306	70.76 308	85.35 314	79.88 333	76.83 322	94.68 318	94.22 328	80.33 321	63.81 331	79.73 332	35.45 340	93.36 325	71.78 324	36.99 337	85.86 328

testpf		88.74 290	89.09 283	87.69 308	95.78 281	83.16 315	84.05 335	94.13 330	85.22 308	90.30 273	94.39 296	74.92 309	95.80 314	89.77 251	93.28 222	84.10 329

PNet_i23d		67.70 310	65.07 311	75.60 322	78.61 335	59.61 340	89.14 330	88.24 339	61.83 331	52.37 335	80.89 330	18.91 343	84.91 336	62.70 333	52.93 332	82.28 330

wuykxyi23d		63.73 314	58.86 316	78.35 321	67.62 340	67.90 335	86.56 332	87.81 340	58.26 333	42.49 339	70.28 337	11.55 346	85.05 335	63.66 332	41.50 333	82.11 331

MVE	62.14 22	63.28 315	59.38 315	74.99 323	74.33 339	65.47 336	85.55 333	80.50 344	52.02 336	51.10 336	75.00 336	10.91 348	80.50 338	51.60 336	53.40 331	78.99 332
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	61.03 23	65.95 311	63.57 313	73.09 325	57.90 342	51.22 343	85.05 334	93.93 331	54.45 334	44.32 338	83.57 327	13.22 344	89.15 333	58.68 335	81.00 311	78.91 333
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		78.40 303	76.75 304	83.38 317	95.54 289	80.43 319	79.42 336	97.40 260	64.67 330	73.46 325	80.82 331	45.65 335	93.14 326	66.32 330	87.43 284	76.56 334
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EMVS		64.07 313	63.26 314	66.53 327	81.73 332	58.81 342	91.85 327	84.75 342	51.93 337	59.09 334	75.13 335	43.32 336	79.09 340	42.03 338	39.47 335	61.69 335

E-PMN		64.94 312	64.25 312	67.02 326	82.28 331	59.36 341	91.83 328	85.63 341	52.69 335	60.22 333	77.28 334	41.06 337	80.12 339	46.15 337	41.14 334	61.57 336

test123		20.95 320	23.72 321	12.64 331	13.54 345	8.19 345	96.55 295	6.13 348	7.48 341	16.74 341	37.98 340	12.97 345	6.05 343	16.69 340	5.43 342	23.68 337

.test1245		73.05 307	76.31 305	63.27 328	87.59 324	75.10 326	96.63 290	96.43 306	82.53 315	80.75 319	92.91 312	68.94 321	93.79 322	68.24 328	12.72 340	20.91 338

testmvs		21.48 319	24.95 320	11.09 332	14.89 344	6.47 346	96.56 293	9.87 347	7.55 340	17.93 340	39.02 339	9.43 349	5.90 344	16.56 341	12.72 340	20.91 338

wuyk23d		30.17 317	30.18 319	30.16 330	78.61 335	43.29 344	66.79 337	14.21 346	17.31 339	14.82 342	11.93 343	11.55 346	41.43 342	37.08 339	19.30 339	5.76 340

cdsmvs_eth3d_5k		23.98 318	31.98 318	0.00 333	0.00 346	0.00 347	0.00 338	98.59 114	0.00 342	0.00 343	98.61 100	90.60 123	0.00 345	0.00 342	0.00 343	0.00 341

pcd_1.5k_mvsjas		7.88 322	10.50 323	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	94.51 60	0.00 345	0.00 342	0.00 343	0.00 341

sosnet-low-res		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

sosnet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

uncertanet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

Regformer		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

ab-mvs-re		8.20 321	10.94 322	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	98.43 115	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

uanet		0.00 323	0.00 324	0.00 333	0.00 346	0.00 347	0.00 338	0.00 349	0.00 342	0.00 343	0.00 344	0.00 350	0.00 345	0.00 342	0.00 343	0.00 341

ESAPD								98.84 54

sam_mvs1												89.45 134

sam_mvs												88.99 145

MTGPA								98.74 78

test_post1							96.68 289				30.43 342	87.85 192	98.69 196	92.59 197

test_post											31.83 341	88.83 157	98.91 178

patchmatchnet-post											95.10 290	89.42 135	98.89 182

MTMP								94.14 329

gm-plane-assit					95.88 278	87.47 304			89.74 276		96.94 237		99.19 142	93.32 174

TEST9					99.31 47	98.50 12	97.92 212	98.73 83	92.63 197	97.74 81	98.68 94	96.20 12	99.80 57

test_8					99.29 55	98.44 14	97.89 220	98.72 85	92.98 188	97.70 84	98.66 97	96.20 12	99.80 57

agg_prior					99.30 52	98.38 17		98.72 85		97.57 93			99.81 50

test_prior4						98.01 41	97.86 223

test_prior2							97.80 228		96.12 63	97.89 75	98.69 92	95.96 25		96.89 71	99.60 49

旧先验2							97.57 245		91.30 245	98.67 36			99.80 57	95.70 115

新几何2							97.64 240

原ACMM2							97.67 238

testdata2													99.89 27	91.65 221

segment_acmp												96.85 3

testdata1							97.32 262		96.34 57

plane_prior7					97.42 183	94.63 192

plane_prior6					97.35 188	94.61 195						87.09 207

plane_prior4											98.28 131

plane_prior3						94.61 195			97.02 39	95.34 153

plane_prior2							98.80 91		97.28 21

plane_prior1					97.37 187

plane_prior						94.60 197	98.44 154		96.74 46						94.22 195

n2								0.00 349

nn								0.00 349

door-mid								94.37 326

test11								98.66 106

door								94.64 325

HQP5-MVS						94.25 210

HQP-NCC					97.20 197		98.05 200		96.43 54	94.45 174

ACMP_Plane					97.20 197		98.05 200		96.43 54	94.45 174

BP-MVS														95.30 125

HQP3-MVS								98.46 141							94.18 197

HQP2-MVS												86.75 213

NP-MVS					97.28 191	94.51 200					97.73 174

MDTV_nov1_ep13			95.40 129		97.48 177	88.34 298	96.85 284	97.29 269	93.74 156	97.48 96	97.26 206	89.18 141	99.05 159	91.92 215	97.43 146

ACMMP++_ref															92.97 224

ACMMP++															93.61 212

Test By Simon												94.64 57

High-resolution multi-view benchmark